Cohere Launches Embed 4 with Best-in-Class Enterprise Embeddings
Cohere has released Embed 4, a new embedding model that tops the MTEB benchmark leaderboard and introduces domain-adaptive embeddings that can be specialized for financial, legal, medical, and technical domains without fine-tuning.
Cohere has released Embed 4, an embedding model that achieves the top score on the Massive Text Embedding Benchmark (MTEB) while introducing a novel domain-adaptive architecture. The model can automatically adjust its embedding strategy based on the content domain, producing superior embeddings for financial, legal, medical, and technical text.
Domain adaptation works through specialized attention heads that activate based on detected content type. When processing a legal document, for example, Embed 4 emphasizes legal terminology, citation patterns, and jurisdictional context. This produces embeddings that are 15-25% more accurate for domain-specific retrieval compared to general-purpose models.
Embed 4 supports 100+ languages and produces embeddings in configurable dimensions from 256 to 4096, allowing users to trade off between accuracy and storage costs. The model also introduces quantized embedding output, reducing storage requirements by 4x with only 1% accuracy loss.
For enterprise RAG applications, Cohere is offering Embed 4 with a new reranking pipeline that combines initial vector search with a cross-encoder reranker. In customer evaluations, the combined pipeline improved retrieval accuracy by 30% compared to embeddings alone, with several financial services firms reporting significant improvements in their document Q&A systems.
Embed 4 is available through Cohere's API at $0.10 per million tokens, a 50% reduction from Embed 3 pricing. The model is also available for on-premises deployment through Cohere's enterprise platform.
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